Joint channel and impulsive noise estimation method for OFDM systems

Xinrong LYU; Youming LI; Mingchen YU

doi:10.11959/j.issn.1000-436x.2018047

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Joint channel and impulsive noise estimation method for OFDM systems

Papers | 更新时间：2024-06-05

- Joint channel and impulsive noise estimation method for OFDM systems
- Journal on Communications Vol. 39, Issue 3, Pages: 191-198(2018)
- 作者机构：
  
  1. 宁波大学信息科学与工程学院，浙江宁波 315211
  2. 浙江工商职业技术学院智能电子学院，浙江宁波 315012
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61571250);The Natural Science Foundation of Ningbo(2015A610121)
- DOI：10.11959/j.issn.1000-436x.2018047
  CLC： TN911.4
- Online First：2018-03，
  
  Published：25 March 2018
- 稿件说明：
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Xinrong LYU, Youming LI, Mingchen YU. Joint channel and impulsive noise estimation method for OFDM systems[J]. Journal on Communications, 2018, 39(3): 191-198.
DOI：

Xinrong LYU, Youming LI, Mingchen YU. Joint channel and impulsive noise estimation method for OFDM systems[J]. Journal on Communications, 2018, 39(3): 191-198. DOI： 10.11959/j.issn.1000-436x.2018047.

摘要

针对 OFDM 系统中的脉冲噪声问题，提出一种基于压缩感知技术的脉冲噪声抑制方法。该方法将信道脉冲响应和脉冲噪声联合视作一个稀疏向量，将发射数据符号视作未知参数，利用稀疏贝叶斯学习理论联合估计信道、脉冲噪声和数据符号。与现有脉冲噪声抑制方法相比，该方法不仅能够充分利用全部子载波信息，而且不需要信道和脉冲噪声的先验统计信息。仿真结果表明，所提方法在信道估计及误比特率性能上有明显改善。

Abstract

Aiming at the impulsive noise occurring in OFDM systems

an impulsive noise mitigation algorithm based on compressed sensing theory was proposed.The proposed algorithm firstly treated the channel impulse response and the impulsive noise as a joint sparse vector by exploiting the sparsity of both them.Then the sparse Bayesian learning framework was adopted to jointly estimate the channel impulse response

the impulsive noise and the data symbols

in which the data symbols were regarded as unknown parameters.Compared with the existing impulsive noise mitigation methods

the proposed algorithm not only utilized all subcarriers but also did not use any a priori information of the channel and impulsive noise.The simulation results show that the proposed algorithm achieves significant improvement on the channel estimation and bit error rate performance.

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references

LIU S , YANG F , DING W , et al . Double kill:compressive-sensing-based narrow-band interference and impulsive noise mitigation for vehicular communications [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 7 ): 5099 - 5109 .

SACUTO F , LABEAU F , AGBA B L . Wide band time-correlated model for wireless communications under impulsive noise within power substation [J ] . IEEE Transactions on Wireless Communications , 2014 , 13 ( 3 ): 1449 - 1461 .

KUAI X , SUN H , ZHOU S , et al . Impulsive noise mitigation in underwater acoustic OFDM systems [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 10 ): 8190 - 8202 .

MAHMOOD A , CHITRE M , ARMAND M A . Detecting OFDM signals in alpha-stable noise [J ] . IEEE Transactions on Communications , 2014 , 62 ( 10 ): 3571 - 3583 .

JUWONO F H , GUO Q , HUANG D , et al . Deep clipping for impulsive noise mitigation in OFDM-based power-line communications [J ] . IEEE Transactions on Power Delivery , 2014 , 29 ( 3 ): 1335 - 1343 .

DARSENA D , GELLI G , MELITO F , et al . ICI-free equalization in OFDM systems with blanking preprocessing at the receiver for impulsive noise mitigation [J ] . IEEE Signal Processing Letters , 2015 , 22 ( 9 ): 1321 - 1325 .

LIN J , NASSAR M , EVANS B L . Impulsive noise mitigation in powerline communications using sparse Bayesian learning [J ] . IEEE Journal on Selected Areas in Communications , 2013 , 31 ( 7 ): 1172 - 1183 .

DUARTE M F , ELDAR Y C . Structured compressed sensing:from theory to applications [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 9 ): 4053 - 4085 .

AL-NAFFOURI T Y , QUADEER A A , CAIRE G . Impulse noise estimation and removal for OFDM systems [J ] . IEEE Transactions on Communications , 2014 , 62 ( 3 ): 976 - 989 .

KORKI M , ZHANG J , ZHANG C , et al . Block-sparse impulsive noise reduction in OFDM systems-a novel iterative bayesian approach [J ] . IEEE Transactions on Communications , 2015 , 64 ( 1 ): 271 - 284 .

PRASAD R , MURTHY C R , RAO B D . Joint approximately sparse channel estimation and data detection in OFDM systems using sparse Bayesian learning [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 14 ): 3591 - 3603 .

ZHENG Z , HAO C , YANG X . Least squares channel estimation with noise suppression for OFDM systems [J ] . Electronics Letters , 2016 , 52 ( 1 ): 37 - 39 .

LIU Y , TAN Z , HU H , et al . Channel estimation for OFDM [J ] . IEEE Communications Surveys ＆ Tutorials , 2014 , 16 ( 4 ): 1891 - 1908 .

CHIEN Y R . Iterative channel estimation and impulsive noise mitigation algorithm for OFDM-based receivers with application to power-line communications [J ] . IEEE Transactions on Power Delivery , 2015 , 30 ( 6 ): 2435 - 2442 .

MEHBOOB A , LI Z , KHANGOSSTAR J , et al . Joint channel and impulsive noise estimation for OFDM based power line communication systems using compressed sensing [C ] // IEEE International Symposium on Power Line Communications and ITS Applications . 2013 : 203 - 208 .

NASSAR M , SCHNITER P , EVANS B L . A factor graph approach to joint OFDM channel estimation and decoding in impulsive noise environments [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 6 ): 1576 - 1589 .

WIPF D P , RAO B D . Sparse Bayesian learning for basis selection [J ] . IEEE Transactions on Signal Processing , 2004 , 52 ( 8 ): 2153 - 2164 .

DING W , YANG F , PAN C , et al . Compressive sensing based channel estimation for OFDM systems under long delay channels [J ] . IEEE Transactions on Broadcasting , 2014 , 60 ( 2 ): 313 - 321 .

DONOHO D L . Compressed sensing [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 4 ): 1289 - 1306 .

WIPF D D , . Sparse estimation with structured dictionaries [C ] // International Conference on Neural Information Processing Systems . 2011 : 2016 - 2024 .

DING W , LU Y , YANG F , et al . Spectrally efficient CSI acquisition for power line communications:a Bayesian compressive sensing perspective [J ] . IEEE Journal on Selected Areas in Communications , 2016 , 34 ( 7 ): 2022 - 2032 .

ITU-R M . Test environment and deployment models-ANNEX2 [S ] . ITU-R , 2000 :24-30.

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